Handwriting recognition using webcam for data entry

This paper presents the development of a system that is robust enough to recognize numerical handwritings with the lowest error. The first test was done with a neural network trained with only the Character Vector Module as its feature extraction method. A result that is far below the set point of the recognition accuracy was achieved, a mere average of 64.67% accuracy. However, the testing were later enhanced with another feature extraction module, which consists of the combination of Character Vector Module, Kirsch Edge Detection Module, Alphabet Profile Feature Extraction Module, Modified Character Module and Image Compression Module. The modules have its distinct characteristics which is trained using the Back-Propagation algorithm to cluster the pattern recognition capabilities among different samples of handwriting. Several untrained samples of numerical handwritten data were obtained at random from various people to be tested with the program. The second tests shows far greater results compared to the first test, have yielded an average of 84.52% accuracy. Further feature extraction modules are being recommended and an additional feature extraction module was added for the third test, which successfully yields 90.67%.